CN113239964A - Vehicle data processing method, device, equipment and storage medium - Google Patents
Vehicle data processing method, device, equipment and storage medium Download PDFInfo
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- 238000012952 Resampling Methods 0.000 claims description 2
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- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
Abstract
The application discloses a method, a device, equipment and a storage medium for processing vehicle data, wherein the method comprises the following steps: acquiring vehicle data, wherein the vehicle data is data generated by running of a vehicle under different working conditions, and the vehicle data is time sequence data; dividing the vehicle data into K sample segments according to the time sequence of the vehicle data, wherein each sample segment in the K sample segments comprises a target characteristic vector, K is a natural number and is more than or equal to 2; clustering each sample segment according to the type of the histogram of the target feature vector to obtain NcA data set, NcIs a natural number, NcNot less than 2; and sampling each data set to obtain sampling data, wherein the sampling data is used for training and testing the target algorithm model. According to the method and the device, each data set is sampled, so that vehicle data required by training and testing are simplified, and the efficiency of training and testing the target algorithm model is improved.
Description
Technical Field
The present application relates to the field of vehicle control technologies, and in particular, to a method, an apparatus, a device, and a storage medium for processing vehicle data.
Background
With the development of vehicle intelligence and networking, data processing through an algorithm model (such as a machine learning model, a digital twin model or a physical model) is increasingly required.
In the related art, vehicle data can be processed through an algorithm model to obtain target parameters to be predicted, so that the vehicle is controlled according to the target parameters. When the algorithm model is established, training data needs to be prepared, and the algorithm model is trained through the training data.
However, since noise or abnormal values often exist in training data, the algorithm model is overfitting due to the fact that the training data participates in training, and the generalization capability of the algorithm model is poor; meanwhile, due to the fact that the training data volume is large, the inference complexity and the inference time of the algorithm model rise exponentially, the algorithm model is difficult to respond immediately, and the processing efficiency is low.
Disclosure of Invention
The application provides a vehicle data processing method, a vehicle data processing device, vehicle data processing equipment and a storage medium, and can solve the problem that an algorithm model is low in training processing efficiency in the related art.
In one aspect, an embodiment of the present application provides a method for processing vehicle data, including:
acquiring vehicle data, wherein the vehicle data are generated by running of the vehicle under different working conditions, and the vehicle data are time sequence data;
dividing the vehicle data into K sample segments according to the time sequence of the vehicle data, wherein each sample segment in the K sample segments comprises a target feature vector, the target feature vector is composed of target data, the type of the target data is the same as that of output data of a target algorithm model, K is a natural number and is more than or equal to 2;
clustering the sample segments according to the type of the histogram of the target feature vector to obtain NcA data set, NcIs a natural number, Nc≥2；
And sampling each data set to obtain sampling data, wherein the sampling data is used for training and testing the target algorithm model.
Optionally, when the target data is data of an indefinite length, before clustering each sample segment according to the type of the target feature vector, the method further includes:
and resampling the target data through a preset sampling frequency to obtain fixed-length target data, wherein the fixed-length target data form the target characteristic vector.
Optionally, the preset sampling frequency is a frequency according to Nyquist sampling theorem.
Optionally, the clustering the sample segments according to the type of the histogram of the target feature vector includes:
integrating the fixed-length target characteristic vector in each sample segment to obtain the integral of the target characteristic vector;
calculating a histogram of the target feature vector according to the integral of the target feature vector;
and clustering the vehicle data according to the type of the histogram of the target feature vector.
Optionally, the calculating a histogram of the target feature vector according to the integral of the target feature vector includes:
normalizing the integral of the target characteristic vector to obtain a normalized integral;
and calculating a histogram of the target feature vector according to the normalized integral.
Optionally, the histogram is described by using a preset number of bins.
Optionally, the method further includes:
and dividing the sampling data into training data and testing data, wherein the training data is used for training the target algorithm model, and the testing data is used for testing the target algorithm model.
Optionally, the training data is more than the test data.
Optionally, the target algorithm model includes a lifting tree model.
In another aspect, an embodiment of the present application provides a processing apparatus, including:
the vehicle data is generated by running the vehicle under different working conditions, and is time sequence data;
the processing module is used for dividing the vehicle data into K sample segments according to the time sequence of the vehicle data, each sample segment in the K sample segments comprises a target feature vector, the target feature vector is composed of target data, the type of the target data is the same as that of output data of a target algorithm model, K is a natural number and is more than or equal to 2; clustering each sample segment according to the type of the histogram of the target feature vector to obtain NcA data set, NcIs a natural number, NcNot less than 2; and sampling each data set to obtain sampling data, wherein the sampling data is used for training and testing the target algorithm model.
In another aspect, the present application provides a computer device, where the device includes a processor and a memory, where the memory stores at least one instruction or program, and the instruction or program is loaded by the processor and executed to implement the vehicle data processing method as described in any one of the above.
In another aspect, the present application provides a computer-readable storage medium, where at least one instruction is stored, and the instruction is loaded and executed by a processor to implement the vehicle data processing method as described in any one of the above.
The technical scheme at least comprises the following advantages:
the vehicle data are divided into K sample segments according to the time sequence, each sample segment is clustered according to the type of a histogram of a target feature vector in each sample segment, and each data set is sampled to obtain sampled data, so that the vehicle data required by training and testing are simplified, and the efficiency of training and testing a target algorithm model is improved; meanwhile, the sampled data are obtained by clustering and sampling according to the histograms of the target characteristic vectors of different types, so that the data coverage range is more comprehensive, and the trained algorithm model is more accurate.
Drawings
In order to more clearly illustrate the detailed description of the present application or the technical solutions in the prior art, the drawings needed to be used in the detailed description of the present application or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a block diagram of a computer device provided by an exemplary embodiment of the present application;
FIG. 2 is a flow chart of a method of processing vehicle data provided by an exemplary embodiment of the present application;
FIG. 3 is a flow chart of a method of processing vehicle data provided by an exemplary embodiment of the present application;
fig. 4 is a block diagram of a processing device provided in an exemplary embodiment of the present application.
Detailed Description
The technical solutions in the present application will be described clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present application, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; the connection can be mechanical connection or electrical connection; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art.
In addition, the technical features mentioned in the different embodiments of the present application described below may be combined with each other as long as they do not conflict with each other.
Referring to fig. 1, a block diagram of a computer device provided in an exemplary embodiment of the present application, which may be a server, a Personal Computer (PC), or an Electronic Control Unit (ECU) equipped in a vehicle, includes: a processor 110 and a memory 120.
The processor 110 may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of a CPU and an NP. The processor 110 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof.
The memory 120 is connected to the processor 110 through a bus or other means, and at least one instruction, at least one program, a code set, or a set of instructions is stored in the memory 120, and is loaded and executed by the processor 110 to implement the vehicle data processing method provided in any one of the following embodiments. The memory 120 may be a volatile memory (volatile memory), a non-volatile memory (non-volatile memory), or a combination thereof. The volatile memory may be a random-access memory (RAM), such as a Static Random Access Memory (SRAM) or a Dynamic Random Access Memory (DRAM). The nonvolatile memory may be a Read Only Memory (ROM), such as a Programmable Read Only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), and an electrically erasable programmable read-only memory (EEPROM). The nonvolatile memory may also be a flash memory (flash memory), a magnetic memory such as a magnetic tape (magnetic tape), a floppy disk (floppy disk), and a hard disk. The non-volatile memory may also be an optical disc.
Referring to fig. 2, a flow chart of a method for processing vehicle data provided by an exemplary embodiment of the present application is shown, the method being executable by a computer device in the embodiment of fig. 1, and the method including:
For example, the vehicle data may be obtained by any of the following: (1) when the server executes the method, vehicle data in the vehicle can be uploaded through an electronic controller in the vehicle, or the vehicle data is uploaded to the server after being acquired manually; (2) when the method is executed by a personal computer, the vehicle data can be acquired manually and then recorded into the personal computer.
For example, the acquired vehicle data includes vehicle data from time 0 to time T, and the acquired vehicle data may be divided into K (K is a preset number) data segments according to the time sequence of the acquired vehicle data. For example, when K is 4, the vehicle data may be divided into data pieces belonging to four time intervals of [0, T1], (T1, T2], (T2, T3], and (T3, T ], where the times T1, T2, T3 e (0, T).
Each data segment comprises target data, the type of the target data is the same as that of output data of the target algorithm model, and a feature vector formed by the target data is a target feature vector. For example, if the target algorithm model is a model that predicts a ratio of air mass to fuel mass in the combustible mixture (which may also be referred to as an excess air ratio, hereinafter simply referred to as "combustible ratio"), the target data is air-fuel ratio data contained in each data segment.
Wherein the histogram reflects the distribution of the target data. For example, the histogram of the target vector of the ith data segment (i is a natural number, i is greater than or equal to 1 and less than or equal to K) can preset the number of Bin descriptions, and each Bin contains the value space of the target data.
The value interval of the preset number of bins is [3, N), and N is a natural number and represents the average number of the target data collected in each data segment. For example, the vehicle data is divided into three data segments (K is 3), the number of the target data collected together is 300, and the average number of the target data in each data segment is 100, so the value range of the preset number is [3,100 ].
And each target data segment is described by a preset number of bins through a histogram, so that the clustering speed can be reduced, and the efficiency can be improved.
Illustratively, as shown in table one, 10 bins (B0, B1, B2, B3, B4, B5, B6, B7, B8, and B9) may be used to describe the histogram of the target vector, taking the first data segment (the data segment whose segID is 01) as an example, the number of target data of Bin distributed to number B0 is 10, the number of target data of Bin distributed to number B1 is 9, the number of target data of Bin distributed to number B2 is 9, the number of target data of Bin distributed to number B3 is 9, the number of target data of Bin distributed to number B4 is 9, the number of target data of Bin distributed to number B5 is 9, the number of target data of Bin distributed to number B6 is 9, the number of target data of Bin distributed to number B7 is 9, the number of target data of Bin distributed to number B8 is 9, and the number of target data of Bin distributed to number B9 is 9.
Watch 1
| B0 | B1 | B2 | B3 | B4 | B5 | B6 | B7 | B8 | B9 | segID |
| 10 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 01 |
| 9 | 10 | 9 | 10 | 9 | 8 | 9 | 8 | 9 | 9 | 02 |
| 9 | 10 | 9 | 9 | 9 | 9 | 8 | 9 | 9 | 9 | 03 |
And step 204, sampling each data set to obtain sampling data, wherein the sampling data is used for training and testing a target algorithm model.
Optionally, after step 204, the method further includes: dividing the sampling data into training data and testing data, wherein the training data is used for training the target algorithm model, and the testing data is used for testing the target algorithm model; optionally, the number of training data is greater than the number of test data.
Alternatively, the target algorithm model may include, but is not limited to, a lifting tree (boosting tree) model.
In summary, in the embodiment of the application, vehicle data is divided into K sample segments according to the time sequence of the vehicle data, each sample segment is clustered according to the type of a histogram of a target feature vector in each sample segment, and each data set is sampled to obtain sampled data, so that the vehicle data required by training and testing is simplified, and the efficiency of training and testing a target algorithm model is improved; meanwhile, the sampled data are obtained by clustering and sampling according to the histograms of the target characteristic vectors of different types, so that the data coverage range is more comprehensive, and the trained algorithm model is more accurate.
In the embodiment of the application, if the target data has different sample lengths in different observations (that is, the target data is data with an indefinite length), the vehicle data may be resampled by a preset sampling frequency (for example, the sampling frequency is a frequency according to Nyquist sampling theorem), so as to obtain target data with a fixed length, and the target feature vector is formed by the target data with the fixed length.
Referring to fig. 3, which shows a flowchart of a processing method of vehicle data provided in an exemplary embodiment of the present application, the method may be executed by a computer device in the embodiment of fig. 1, and the method may be an optional implementation of step 203 in the embodiment of fig. 2, and the method includes:
By integrating the target feature vector, monotonically increasing data can be obtained, thereby facilitating later data processing.
Optionally, if only the shape of the output curve is concerned, the integral of the target feature vector may be normalized to obtain a normalized integral; and calculating a histogram of the target feature vector according to the normalized integral.
And calculating a histogram of the target feature vector according to the normalized integral.
And step 303, clustering the vehicle data according to the type of the histogram of the target feature vector.
Referring to fig. 4, a block diagram of a processing device provided in an exemplary embodiment of the present application is shown, and the processing device may be implemented as a computer device in the above embodiments through software, hardware or a combination of the two, and the processing device includes:
an acquisition module 410 for acquiring vehicle data.
The processing module 420 is configured to divide the vehicle data into K sample segments according to a time sequence thereof, where each sample segment of the K sample segments includes a target feature vector; clustering each sample segment according to the type of the histogram of the target feature vector to obtain NcA data set; and sampling each data set to obtain sampled data, wherein the sampled data is used for training and testing a target algorithm model.
Optionally, the processing module 420 is further configured to resample the vehicle data by using a preset sampling frequency to obtain fixed-length target data, where the fixed-length target data constitutes a target feature vector.
Optionally, the preset sampling frequency is a frequency according to Nyquist sampling theorem.
Optionally, the processing module 420 is further configured to integrate the target feature vector in each sample segment to obtain an integral of the target feature vector; calculating a histogram of the target feature vector according to the integral of the target feature vector; and clustering the vehicle data according to the type of the histogram of the target feature vector.
Optionally, the processing module 420 is further configured to normalize the integral of the target feature vector to obtain a normalized integral; and calculating a histogram of the target feature vector according to the normalized integral.
Optionally, the histogram is described by using a preset number of bins.
Optionally, the processing module 420 is further configured to divide the sampling data into training data and test data, where the training data is used to train the target algorithm model, and the test data is used to test the target algorithm model.
Optionally, the training data is more than the test data.
Optionally, the target algorithm model comprises a lifting tree model.
The present application further provides a computer readable storage medium having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by the processor to implement the method of processing vehicle data as described in any of the above embodiments.
The application also provides a computer program product which can be used for causing a computer to execute the vehicle data processing method provided by the above method embodiments when the computer program product runs on the computer.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of this invention are intended to be covered by the scope of the invention as expressed herein.
Claims (12)
1. A method of processing vehicle data, comprising:
acquiring vehicle data, wherein the vehicle data are generated by running of the vehicle under different working conditions, and the vehicle data are time sequence data;
dividing the vehicle data into K sample segments according to the time sequence of the vehicle data, wherein each sample segment in the K sample segments comprises a target feature vector, the target feature vector is composed of target data, the type of the target data is the same as that of output data of a target algorithm model, K is a natural number and is more than or equal to 2;
clustering the sample segments according to the type of the histogram of the target feature vector to obtain NcA data set, NcIs a natural number, Nc≥2；
And sampling each data set to obtain sampling data, wherein the sampling data is used for training and testing the target algorithm model.
2. The method of claim 1, wherein before clustering each of the sample segments according to the type of the target feature vector when the target data is a feature vector of indefinite length, further comprising:
and resampling the vehicle data through a preset sampling frequency to obtain fixed-length target data, wherein the fixed-length target data form the target characteristic vector.
3. The method of claim 2, wherein the preset sampling frequency is a frequency that complies with the Nyquist sampling theorem.
4. The method of claim 3, wherein clustering the sample segments according to the type of histogram of the target feature vector comprises:
integrating the target characteristic vector in each sample segment to obtain the integral of the target characteristic vector;
calculating a histogram of the target feature vector according to the integral of the target feature vector;
and clustering the vehicle data according to the type of the histogram of the target feature vector.
5. The method of claim 4, wherein said computing a histogram of the target feature vector based on an integral of the target feature vector comprises:
normalizing the integral of the target characteristic vector to obtain a normalized integral;
and calculating a histogram of the target feature vector according to the normalized integral.
6. The method of claim 5, wherein the histogram is described by a predetermined number of bins.
7. The method of any of claims 1 to 6, further comprising:
and dividing the sampling data into training data and testing data, wherein the training data is used for training the target algorithm model, and the testing data is used for testing the target algorithm model.
8. The method of claim 7, wherein the training data is greater than the test data.
9. The method of claim 7, wherein the target algorithm model comprises a lifting tree model.
10. A processing apparatus, comprising:
the vehicle data is generated by running the vehicle under different working conditions, and is time sequence data;
the processing module is used for dividing the vehicle data into K sample segments according to the time sequence of the vehicle data, each sample segment in the K sample segments comprises a target feature vector, the target feature vector is composed of target data, the type of the target data is the same as that of output data of a target algorithm model, K is a natural number and is more than or equal to 2; clustering each sample segment according to the type of the histogram of the target feature vector to obtain NcA data set, NcIs a natural number, NcNot less than 2; sampling each data set to obtain sampling numberAccordingly, the sampled data is used to train and test the target algorithm model.
11. A computer device, characterized in that it comprises a processor and a memory in which at least one instruction or program is stored, which is loaded and executed by the processor to implement a method of processing vehicle data according to any one of claims 1 to 9.
12. A computer-readable storage medium having stored therein at least one instruction, which is loaded and executed by a processor to implement a method of processing vehicle data according to any one of claims 1 to 9.
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